Brief Customer Segmentation with Kmeans

This repository contains a comprehensive analysis of telecom user behavior and engagement. It includes: - User Overview Analysis: Identifies top handsets and manufacturers, explores user behavior across various applications, and performs dimensionality reduction for deeper insights. - User Engagement Analysis: Evaluates user engagement

Text classification and topic extraction from COVID-19 articles

Economic_DER_Integration is a Economical demand-side management with distributed energy resources project which Demand-side management (DSM) and distributed energy resources (DER) are both strategies that can help reduce energy costs and improve the efficiency of energy.

Using penguin dataset collected from Palmer Station, Antarctica, this project applies data science techniques to identify and group penguins based on physical traits in the absence of species labels

RAYAN AI international competition training course : Homeworks and Projects

This repository features three data science tasks from GRIP October'23: Linear Regression on student scores, K-Means Clustering on the Iris dataset, and Exploratory Data Analysis on a retail dataset.

This project demonstrates the use of the K-Means clustering algorithm on the Iris dataset, a classic dataset in machine learning. It includes code for loading the dataset, determining the optimal number of clusters using the Elbow Method, applying K-Means clustering, and visualizing the resulting clusters and centroids.

Applying Principal Component Analysis for image compression, exploring how varying numbers of principal components affect image quality and compression ratio.

Vault of variety of topics taught for Rayan Contest

Employing unsupervised learning techniques to cluster Italian wines grown by three different cultivars

In this project, unsupervised methods were employed to form clusters of similar vehicles based on sales data from Italy between 2003 and 2005. Through detailed analysis of monthly sales volumes, vehicles were grouped to reveal competitive relationships. This approach aids in understanding market dynamics and identifying key competitors.

Cluster Cryptocurrencies with K-Means, Finding Elbow Curve, Optimize Clusters with Principal Component Analysis.

Customer Segmentation using R

Tugas praktikum Data Mining I

Classification Model of Potential Credit Card Customers

👬 The aim for this project is to segment customers. The segmentation was done based on RFM as well as K-means clustering using SQL and Python programming language.

A new clustering technique is proposed that incorporates outliers during clustering. The proposed approach involves using a variable, (λ > 0), to define the cluster radius. Weighted an

Analysis to optimize services & resident satisfaction in senior living facilities by segmenting population based on characteristics & behaviors.